Risk assessment of asset leaks in a blockchain

ABSTRACT

A system and method for asset leak risk assessment in blockchains are presented. A risk assessment of recursive call attack vulnerabilities may be cognitively determined according to risk vulnerability measurements generated from a computer program source code, a list of external call functions, a risk assessment function, a list of assets, a parser, or a combination thereof.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates in general to computing systems, and moreparticularly to, various embodiments for assessing risk of asset leaksin a blockchain by a processor.

Description of the Related Art

In today's society, consumers, business persons, educators, and othersuse various computing network systems with increasing frequency in avariety of settings. Computer systems may be found in the workplace, athome, or at school. Computer systems may include data storage systems,or disk storage systems, to process and store data. In recent years,both software and hardware technologies have experienced amazingadvancement. With the new technology, more and more functions are addedand greater convenience is provided for use with these computingsystems.

SUMMARY OF THE INVENTION

Various embodiments for asset leak risk assessment in Turing-completeblockchains using one or more processors are provided. In oneembodiment, by way of example only, a method for assessing risk of assetleaks in a blockchain, again by a processor, is provided. A riskassessment of recursive call attack vulnerabilities may be cognitivelydetermined according to risk vulnerability measurements generated from acomputer program source code, a list of external call functions, a riskassessment function, a list of assets, a parser, or a combinationthereof.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of the invention will be readilyunderstood, a more particular description of the invention brieflydescribed above will be rendered by reference to specific embodimentsthat are illustrated in the appended drawings. Understanding that thesedrawings depict only typical embodiments of the invention and are nottherefore to be considered to be limiting of its scope, the inventionwill be described and explained with additional specificity and detailthrough the use of the accompanying drawings, in which:

FIG. 1 is a block diagram depicting an exemplary computing nodeaccording to an embodiment of the present invention;

FIG. 2 is an additional block diagram depicting an exemplary cloudcomputing environment according to an embodiment of the presentinvention;

FIG. 3 is an additional block diagram depicting abstraction model layersaccording to an embodiment of the present invention;

FIG. 4 is an additional block diagram depicting an exemplary functionalrelationship between various aspects of the present invention;

FIG. 5 is a flowchart diagram depicting an additional exemplary methodfor assessing asset leak risks in Turing-complete blockchains by aprocessor, again in which aspects of the present invention may berealized;

FIG. 6 is a block diagram an additional block diagram depictingexecution queue of vulnerable source code and execution queue of updatedsource code various aspects of the present invention; and

FIG. 7 is an additional flowchart diagram depicting an exemplary methodfor assessing asset leak risks in a blockchain (e.g., Turing-completeblockchains) by a processor, again in which aspects of the presentinvention may be realized.

DETAILED DESCRIPTION OF THE DRAWINGS

A blockchain is a distributed database that may be used to maintain atransaction ledger. A transaction ledger may denote an ordered set oftransactions that have been validated or confirmed within a system up toa certain point in time. A transaction ledger may include acontinuously-growing list of data records, where each data record mayinclude data relating to one transaction. Further, encryption and othersecurity measures may be used to secure the transaction ledger fromtampering and revision. The blockchain may include a number of blocks,each block holding one or more individual transactions or data records.Further, each block may contain a timestamp and a link to a previousblock. A blockchain network may be used and enabled users may be allowedto connect to the network, send new transactions to the blockchain,verify transactions, and/or create new blocks.

A subset of blockchain platforms are Turing-complete systems that allowthe storage of assets that can be managed with the use of applicationagents (e.g., “software agents”) that are encoded on the blockchain. Inone aspect, “assets” or “Blockchain assets” are a type of digital assetor cryptocurrency, and sometimes represent stakes in a particularproject or company. This means that asset as used herein may representany digital entity that can be immediately transferred such as, forexample, cryptocurrency or other digital value. The amount and value ofthe asset of the blockchain itself is modified with a call and executionof the application agents. Hence, for example, an asset containingcrypto-currencies, is enabled to know the crypto-currency value to knowan increase or decrease of the crypto-currency value with the combinedactions of multiple and heterogeneous software agents.

If an application agent contains recursive functions with depleting orinflating skills/assets, the application agent can be exploited togenerate a recursive call attack, that, if unchecked, causes the leak ofthe asset.

Accordingly, the present invention provides a solution for assessing therisk of asset leaks in a blockchain. A risk assessment of recursive callattack vulnerabilities may be cognitively determined according to riskvulnerability measurements generated from a computer program sourcecode, a list of external call functions, a risk assessment function, alist of assets, a parser, or a combination thereof.

In one aspect, the risk of recursive call attacks in Turing-completeblockchains may be assessed. A probability of depleting assets withsoftware agents on Turing-complete blockchains may be assessed andmeasured. A usage of profiling tools, time windows computation and riskassessment may be combined to advise on the probability of assetsdepletion, on Turing-complete blockchains, via the exploitation ofrecursive functions.

In an additional aspect, the present invention provides for detectingrecursive call attacks such as, for example, recursive call attacks inan asset management environment with one or more application agents. Atime window may be determined and a risk of re-entry calls may beassessed using values of the assets stored on a blockchain.

Also, as used herein, a computing system may include large scalecomputing called “cloud computing” in which resources may interactand/or be accessed via a communications system, such as a computernetwork. Resources may be software-rendered simulations and/oremulations of computing devices, storage devices, applications, and/orother computer-related devices and/or services run on one or morecomputing devices, such as a server. For example, a plurality of serversmay communicate and/or share information that may expand and/or contractacross servers depending on an amount of processing power, storagespace, and/or other computing resources needed to accomplish requestedtasks. The word “cloud” alludes to the cloud-shaped appearance of adiagram of interconnectivity between computing devices, computernetworks, and/or other computer related devices that interact in such anarrangement.

It should be noted that one or more computations or calculations may beperformed using various mathematical operations or functions that mayinvolve one or more mathematical operations (e.g., solving differentialequations or partial differential equations analytically orcomputationally, using addition, subtraction, division, multiplication,standard deviations, means, averages, percentages, statistical modelingusing statistical distributions, by finding minimums, maximums orsimilar thresholds for combined variables, etc.).

Other examples of various aspects of the illustrated embodiments, andcorresponding benefits, will be described further herein.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment and/orcomputing systems associated with one or more vehicles. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,system memory 28 may include at least one program product having a set(e.g., at least one) of program modules that are configured to carry outthe functions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in system memory 28 by way of example, and not limitation,as well as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Device layer 55 includes physical and/or virtual devices, embedded withand/or standalone electronics, sensors, actuators, and other objects toperform various tasks in a cloud computing environment 50. Each of thedevices in the device layer 55 incorporates networking capability toother functional abstraction layers such that information obtained fromthe devices may be provided thereto, and/or information from the otherabstraction layers may be provided to the devices. In one embodiment,the various devices inclusive of the device layer 55 may incorporate anetwork of entities collectively known as the “internet of things”(IoT). Such a network of entities allows for intercommunication,collection, and dissemination of data to accomplish a great variety ofpurposes, as one of ordinary skill in the art will appreciate.

Device layer 55 as shown includes sensor 52, actuator 53, “learning”thermostat 56 with integrated processing, sensor, and networkingelectronics, camera 57, controllable household outlet/receptacle 58, andcontrollable electrical switch 59 as shown. Other possible devices mayinclude, but are not limited to various additional sensor devices,networking devices, electronics devices (such as a remote controldevice), additional actuator devices, so called “smart” appliances suchas a refrigerator or washer/dryer, and a wide variety of other possibleinterconnected objects.

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provides cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provides pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and, in the context of the illustratedembodiments of the present invention, various workloads and functions 96for asset leak risk assessment in a blockchain. In addition, workloadsand functions 96 for asset leak risk assessment in a blockchain mayinclude such operations as data analytics, data analysis, and as will befurther described, notification functionality. One of ordinary skill inthe art will appreciate that the workloads and functions 96 for assetleak risk assessment in a blockchain may also work in conjunction withother portions of the various abstractions layers, such as those inhardware and software 60, virtualization 70, management 80, and otherworkloads 90 (such as data analytics processing 94, for example) toaccomplish the various purposes of the illustrated embodiments of thepresent invention.

Turning now to FIG. 4, a block diagram depicting exemplary functionalcomponents 400 according to various mechanisms of the illustratedembodiments is shown. FIG. 4 illustrates a system 400 for asset leakrisk assessment in Turing-complete blockchains in a computingenvironment. As will be seen, many of the functional blocks may also beconsidered “modules” or “components” of functionality, in the samedescriptive sense as has been previously described in FIGS. 1-3. Withthe foregoing in mind, the module/component blocks 400 may also beincorporated into various hardware and software components of a systemfor cognitive data curation in accordance with the present invention.Many of the functional blocks 400 may execute as background processes onvarious components, either in distributed computing components, or onthe user device, or elsewhere.

Computer system/server 12 of FIG. 1 is shown incorporating an asset leakrisk assessment service 410. The asset leak risk assessment service 410may incorporate processing unit 16 (“processor”) and memory 28 of FIG. 1to perform various computational, data processing and otherfunctionality in accordance with various aspects of the presentinvention. The asset leak risk assessment service 410 may be provided bythe computer system/server 12 of FIG. 1.

As one of ordinary skill in the art will appreciate, the depiction ofthe various functional units in the asset leak risk assessment service410 is for purposes of illustration, as the functional units may belocated within the asset leak risk assessment service 410 or elsewherewithin and/or between distributed computing components.

In one aspect, the computer system/server 12 and/or the asset leak riskassessment service 410 may provide virtualized computing services (i.e.,virtualized computing, virtualized storage, virtualized networking,etc.). More specifically, the asset leak risk assessment service 410 mayprovide, and/or be included in, a virtualized computing, virtualizedstorage, virtualized networking and other virtualized services that areexecuting on a hardware substrate.

The asset leak risk assessment service 410 may also function as adatabase and/or service that may store, maintain, and update data,services, and/or resources internal to and/or external to a cloudcomputing environment such as described in FIG. 2. In one aspect, assetleak risk assessment service 410 may assist in providing access toand/or assist in performing one or more various types of data, servicesand/or resources. In one aspect, the asset leak risk assessment service410 may provide a parser component 440, an external call analyzercomponent 450, a variable profiler component 460, a variable checkercomponent 470, and a risk assessment component 480.

The parser component 440, given a particular computer programming sourcecode (“source code”), may generate/output a list of external calls, anexecution queue, and a dependency map for variables. The parsercomponent 440 may read input source code that feeds asset leak riskassessment service 410 with a queue consisting variable modificationsand function calls, referred as “Execution Queue,” and a variablerelation map (e.g., a dependency map).

The external call analyzer component 450 may analyze and profile a timeof an external call and generate a list of the variables that theexternal call invokes.

The variable profiler component 460 may generate a list of functions andmay profile a time for a selected variable. That is, the variableprofiler component 460 may profile a time of an external call andgenerate a list of variables invoked by the external call.

The variable checker component 470 may executes a choice of variablesbased on logic instructions.

The risk assessment component 480 may determine and/or compute avulnerability risk based on a given function of assets value, timeprofiles and how many times a variable appears in a suspicious list.That is, the risk assessment component 480 may cognitively determine arisk assessment of recursive call attack vulnerabilities according torisk vulnerability measurements generated from a computer program sourcecode, a list of external call functions, a risk assessment function, alist of assets, a parser, or a combination thereof.

The risk assessment component 480 may leverage (optionally) user definedfunctions that may be used to quantify (according to a context, userpreferences, a user experience, etc.) a risk associated with the variouscharacteristics detected. In one aspect, the functions may be linear,exponential, geometric (e.g., risk increasing linearly, exponentially orgeometrically depending on the time call is spent on the recursion).

The risk assessment component 480 may determine the risk assessment ofrecursive call attack vulnerabilities according to a selected callfunction of asset values, one or more time profiles, and a number oftimes a variable appears in a suspicious list. In an additional aspect,the risk assessment component 480 may determine a probability ofdepleting assets from a blockchain according to the risk vulnerabilitymeasurements (e.g., depleting assets of a blockchain via a crafted smartcontract or a recursive call attack on blockchain exploitingvulnerabilities in the source code). For example, the risk assessmentcomponent 480 may analyze the source code of a smart contract and returnan associated risk probability.

Turning now to FIG. 5, a method 500 for asset leak risk assessment inTuring-complete blockchains by a processor is depicted, in which variousaspects of the illustrated embodiments may be implemented. Thefunctionality 500 may be implemented as a method executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitorymachine-readable storage medium. Also, one or more components,functionalities, and/or features of FIGS. 1-4 may be implemented in FIG.5. The functionality 500 may start in block 502.

User input data 504 (e.g., user input) may be collected and/or sent to aparser (e.g., the parser component 440 of FIG. 4), as in block 506. Theuser input data 504 may include, but not limited to, 1) one or morecomputing program source code, 2) a list of external calls, 3) a list ofassets, and/or 4) a risk assessment function. The parser maygenerate/output one or more execution queues, and dependency map forvariables, and also initialize a suspicious map, as in block 508. Fromblock 508, an external call analyzer (e.g., the external call analyzercomponent 450 of FIG. 4) may receive the output of the parser andanalyze and profile a time (e.g., a time profiler operation) of anexternal call and generate a list of the variables that the externalcall invokes (e.g., time profiler external call “T_EC”), as in block512. The time profiler operation is a function that may analyze one ormore recursive calls and identify the time windows of each of therecursive calls. That is, time profiler operation may profile andindicate the time windows of each of the recursive calls indicating howlong each recursive call remains active (e.g., how long variables remainin a function). The variable profiler (e.g., the variable profilercomponent 460 of FIG. 4) may receive the data from block 512, as inblock 514, and may generate a list of functions (e.g., function callname: “FCX”) and profiles a time for a selected variable (e.g., timeprofile variable: “T_FCX”), as in block 516.

A variable checker (e.g., the variable checker component 470 of FIG. 4)may determine if any of the variables have been modified, as in block518. Thus, not only does the variable checker perform the criticaloperation to determine those variables in a function and the amount oftime (e.g., how long) the variables remain active, but the variablechecker component also checks if the variables have been modified. Thepresence of a modified variable indicates one or more assets in theblockchain are being drained. The variable checker may determine whetherthe time of the modification of the variable is less than the time ofthe external call (e.g., “T_CX”<“T_EC”), and the time profiler operationmay add the variable, the time, and the counter to a suspicious map, asin block 520. That is, “the time of the modification of the variable isless than the time of the external call” (e.g., “T_CX”<“T_EC”) meansthat the time taken by the execution of the source code to update thevalue of the variable should be less than the time taken by theexecution of code to access the variable from outside the executingcode. In this example, “T_CX”<“T_EC” means the variable is involved inthe source code executed outside the variable normal lifecycle withinthe blockchain, which may be used as indication of a potential threat.

Upon reaching the end of the variable list, a next or subsequentexternal call in the execution list may be retrieved (e.g., retrievingfurther input), as in block 522, and the method 500 moves back to block510 and/or sends to the external call analyzer a request for the nextexternal call. Upon reaching the end of the external calls, a riskassessment function may be determined or predicted, as in block 524. Alist of potential threats (e.g., future threat), and any associatedrisk, may be generated and/or provided, as in block 526. Because thecomputing system is an offline, the risks of such attacks may beanalyzed offline and may ignore the current depletion since thedepletion of an asset is a future call (e.g., occurring/happening afterthe execution of the current smart contract). The functionality 500 mayend, as in block 528. It should be noted that FIG. 5 may be performed inan “offline” environment.

Turning now to FIG. 6, a block diagram depicting execution queue ofvulnerable source code and execution queue of updated source codeaccording to various mechanisms of the illustrated embodiments is shown.That is, FIG. 6 depicts, by way of example only, a recursive call attackon an Ethereum Blockchain exploiting vulnerabilities in the source codeof a DAO “Decentralized Autonomous Organization” smart contract. A DAOmay codify one or more rules/decision making system of an organization,eliminating the need for documents and person in governing and creatinga structure with decentralized control. In one aspect, the Ethereumnetwork is a network of computers all running the Ethereum blockchain.The blockchain enables the exchange of tokens of value, called ether or“assets.” In one aspect, the Ethereum enables a user to write and put onthe network one or more smart contracts and the general-purpose codethat executes on every computer in the network.

However, it should be noted that a major reason behind a success for anattack for a smart contract is that the user may not been able toidentify a recursive call attack vulnerability. Thus, in FIG. 6 at block602A, an Execution Queue of the vulnerable source code is displayed anddisplays the mechanisms of the present invention in operation forhandling the attack.

The execution queue of vulnerable source code block 602A depicts aglobal variable paidOut[ ] function and may be checked by an algorithmglobal variable check component (e.g., variable checker component 470 asdescribed in FIG. 4). That is, the global variable “paidOut[ ]” has beenmodified before the external call, but does not generate anyvulnerabilities. It should be noted that the reason why the modifiedvariable before the external call does not generate vulnerabilities isbecause the modification of the global variable “paidOut[ ]” before theexternal call (recipient.call( )) is that both modifications (e.g., readand update) occur before the external call, which can cause a bug to beexploited.

f

However, the global variable “paidOut[ ]” has been modified after theexternal call, while the function calls “withdrawRewardFor( )” is stillactive (e.g., “alive”). That is, block 602B has the update(paidOut[account]+=reward) after the external call, which can causeincorrect execution of such code. In one aspect, as indicated in thevulnerable source code 602A, the global variable paidOut[ ] function andmay be checked by an algorithm global variable check component (e.g.,variable checker component 470 as described in FIG. 4). That is, since avariable “V” has been modified after the external call, variable checkercomponent 470 may classify the global variable “paidOut[ ]” asvulnerable and may send the global variable “paidOut[ ]” along with theactive function calls “withdrawRewardFor( )” and “splitDAO( )” to asuspicious map. After the attack, one or more application programmersand/or machine learning operation may repair and/or correct thevulnerability in the source code, and may provide the execution queue ofthe updated code, and confirms security of the updated code.

In the execution queue of updated source code block 602B, the globalvariable “paidOut[ ]” has been modified at least two times prior beforethe external call. However, these modifications do not generate anyvulnerabilities. Since the modified the global variable “paidOut[ ]” hasnot been modified after external call, the global variable “paidOut[ ]”will not be sent to the suspicious map by the variable checker component470.

Turning now to FIG. 7, a method 700 for asset leak risk assessment in ablockchain (e.g., Turing-complete blockchains) by a processor isdepicted, in which various aspects of the illustrated embodiments may beimplemented. The functionality 700 may be implemented as a methodexecuted as instructions on a machine, where the instructions areincluded on at least one computer readable medium or one non-transitorymachine-readable storage medium. The functionality 700 may start inblock 702.

A risk assessment of recursive call attack vulnerabilities may becognitively determined according to risk vulnerability measurementsgenerated from a computer program source code, a list of external callfunctions, a risk assessment function, a list of assets, a parser, or acombination thereof, as in block 704. The functionality 700 may end, asin block 706.

In one aspect, in conjunction with and/or as part of at least one blockof FIG. 7, the operations of method 700 may include each of thefollowing. The operations of method 700 may use the one or morelicensing tokens from the pool of licensing tokens upon initiating alogin operation to the one or more applications. One or more licensingtokens may be returned to the pool of licensing tokens upon terminatinguse of the one or more applications, or the one or more licensing tokensmay be used, returned to the pool of licensing tokens, by an alternativeuser for the one or more applications, an alternative application, or acombination thereof.

The operations of method 700 may extract usage data of the one or moreapplications and/or convert the usage data into a blockchain datastructure for storing in the transactional database. The usage data ofthe one or more applications may be recorded in the transactionaldatabase.

The usage data storage may be stored in the transactional database viaan interactive graphical user interface (GUI) of a computing device. Theoperations of method 700 may determine usage of the one or moreapplications exceeds a total number of the pool of licensing tokensusing the transactional database.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowcharts and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowcharts and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowcharts and/or block diagram block orblocks.

The flowcharts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowcharts or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustrations, and combinations ofblocks in the block diagrams and/or flowchart illustrations, can beimplemented by special purpose hardware-based systems that perform thespecified functions or acts or carry out combinations of special purposehardware and computer instructions.

1. A method for asset leak risk assessment in blockchains by aprocessor, comprising: cognitively determining a risk assessment ofrecursive call attack vulnerabilities according to risk vulnerabilitymeasurements generated from a computer program source code, a list ofexternal call functions, a risk assessment function, a list of assets, aparser, or a combination thereof.
 2. The method of claim 1, whereincognitively determining the risk assessment of recursive call attackvulnerabilities further includes determining the risk assessment ofrecursive call attack vulnerabilities according to a selected callfunction of asset values, one or more time profiles, and a number oftimes a variable appears in a suspicious list.
 3. The method of claim 1,wherein cognitively determining the risk assessment of recursive callattack vulnerabilities further includes determining a probability ofdepleting assets from a blockchain according to the risk vulnerabilitymeasurements.
 4. The method of claim 1, further including generating,via the parser, the list of external call functions, an execution queue,a dependency map for one or more variables.
 5. The method of claim 1,further including profiling a time of an external call and generating alist of variables invoked by the external call.
 6. The method of claim1, further including generating a list of functions and profiling a timefor a selected variable.
 7. The method of claim 1, further includingdetermining a vulnerability risk according to a selected function ofasset values, one or more time profiles, a number of times a variableappears in a suspicious list.
 8. A system for asset leak risk assessmentin, comprising: one or more computers with executable instructions thatwhen executed cause the system to: cognitively determine a riskassessment of recursive call attack vulnerabilities according to riskvulnerability measurements generated from a computer program sourcecode, a list of external call functions, a risk assessment function, alist of assets, a parser, or a combination thereof.
 9. The system ofclaim 8, wherein, pursuant to cognitively determining the riskassessment of recursive call attack vulnerabilities, the executableinstructions further determine the risk assessment of recursive callattack vulnerabilities according to a selected call function of assetvalues, one or more time profiles, and a number of times a variableappears in a suspicious list.
 10. The system of claim 8, wherein,pursuant to cognitively determining the risk assessment of recursivecall attack vulnerabilities, the executable instructions furtherdetermine a probability of depleting assets from a blockchain accordingto the risk vulnerability measurements.
 11. The system of claim 8,wherein the executable instructions further generate, via the parser,the list of external call functions, an execution queue, a dependencymap for one or more variables.
 12. The system of claim 8, wherein theexecutable instructions further profile a time of an external call andgenerating a list of variables invoked by the external call.
 13. Thesystem of claim 8, wherein the executable instructions further generatea list of functions and profiling a time for a selected variable. 14.The system of claim 8, wherein the executable instructions furtherdetermine a vulnerability risk according to a selected function of assetvalues, one or more time profiles, a number of times a variable appearsin a suspicious list.
 15. A computer program product for asset leak riskassessment in blockchains by a processor, the computer program productcomprising a non-transitory computer-readable storage medium havingcomputer-readable program code portions stored therein, thecomputer-readable program code portions comprising: an executableportion that cognitively determines a risk assessment of recursive callattack vulnerabilities according to risk vulnerability measurementsgenerated from a computer program source code, a list of external callfunctions, a risk assessment function, a list of assets, a parser, or acombination thereof.
 16. The computer program product of claim 15,wherein, pursuant to cognitively determining the risk assessment ofrecursive call attack vulnerabilities, further including: an executableportion that determines the risk assessment of recursive call attackvulnerabilities according to a selected call function of asset values,one or more time profiles, and a number of times a variable appears in asuspicious list; or an executable portion that determine a probabilityof depleting assets from a blockchain according to the riskvulnerability measurements.
 17. The computer program product of claim15, further including an executable portion that generates, via theparser, the list of external call functions, an execution queue, adependency map for one or more variables.
 18. The computer programproduct of claim 15, further including an executable portion thatprofiles a time of an external call and generating a list of variablesinvoked by the external call.
 19. The computer program product of claim15, further including an executable portion that generates a list offunctions and profiling a time for a selected variable.
 20. The computerprogram product of claim 15, further including an executable portionthat determining a vulnerability risk according to a selected functionof asset values, one or more time profiles, a number of times a variableappears in a suspicious list.